A new approach for selecting the number of factors

Computational Statistics and Data Analysis

Volumn 54, Issue 12, 2010, Pages 2990-2998

  Chen, Yin Ping ^a   Huang, Hsin Cheng ^b   Tu, I Ping ^b  

^a NATIONAL CENTRAL UNIVERSITY   (Taiwan)

^b INSTITUTE OF STATISTICAL SCIENCE   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

DEGREES OF FREEDOM (MECHANICS); RISK PERCEPTION;

MODEL SELECTION; NEW APPROACHES; NUMBER OF FACTORS; RISK ESTIMATION;

PERTURBATION TECHNIQUES;

EID: 77955409533     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2009.10.002     Document Type: Article

References (29)

1. Akaike, H., 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716-723.
2. Akaike, H., 1987. Factor analysis and AIC. Psychometrika 52, 317-332.
3. Bartholomew, D.J., Knott, M., 1999. Latent Variable Models and Factor Analysis, 2nd ed. Arnold, London.
4. Bartlett, M.S., 1937. The statistical conception of mental factors. British Journal of Psychology 28, 97-104.
5. Bartlett, M.S., 1938. Methods of estimating mental factors. Nature 141, 609-610.
6. Bartlett, M.S., 1950. Tests of significance in factor analysis. British Journal of Psychology (Statistical Section) 3, 77-85.
7. Bozdogan, H., Ramirez, D.E., 1987. An expert model selection approach to determine the best pattern structure in factor analysis models. In: Bozdogan, H., Gupta, A.K. (Eds.), Multivariate Statistical Modeling and Data Analysis. Reidel Publishing Company, pp. 35-60.
8. Bozdogan, H., Shigemasu, K., 1998. Bayesian factor analysis model and choosing the number of factors using a new informational complexity criterion. Technical Report. Department of Statistics, University of Tennessee.
9. Cattell, R.B., 1966. The scree test for the number of factors. Multivariate Behavioral Research 1, 246-276.
10. Dhrymes, P.J., Friend, I., Glutekin, N.B., 1984. A critical reexamination of the empirical evidence on the arbitrage pricing theory. Journal of Finance 39, 323-346.
11. Efron, B., 2004. The estimation of prediction error: Covariance penalties and cross-validation (with discussion). Journal of the American Statistical Association 99, 619-642.
12. Fachel, G.M.G., 1986. The C-type distribution as an underlying model for categorical data and its use in factor analysis. Ph.D. thesis, University of London.
13. Guttman, L., 1954. Some necessary conditions for common factor analysis. Psychometrika 19, 149-162.
14. Guttman, L., 1956. Best possible systemic estimates of communalities. Psychometrika 21, 273-285.
15. Hendrickson, A.E., White, P.O., 1964. Promax: A quick method for rotation to oblique simple structure. British Journal of Statistical Psychology 17, 65-70.
16. Holzinger, K.J., Swineford, F., 1939. A study in factor analysis: The stability of a bi-factor solution. In: Supplementary Educational Monographs 48. University of Chicago.
17. Huang, H-C., Chen, C-S., 2007. Optimal geostatistical model selection. Journal of the American Statistical Association 102, 1009-1024.
18. Jöreskog, K.G., 1963. Statistical Estimation in Factor Analysis. Almqvist & Wilksell, Uppsala.
19. Kaiser, H.F., 1958. The varimax criterion for analytic rotation in factor analysis. Psychometrika 23, 187-200.
20. Kaiser, H.F., 1960. The application of electronic computer to factor analysis. Educational and Psychological Measurement 20, 141-151.
21. Krijnen, W.P., 2006. Convergence of estimates of unique variances in factor analysis based on the inverse sample covariance matrix. Psychometrika 71, 193-199.
22. Lawley, D.N., Maxwell, A.E., 1971. Factor Analysis as a Statistical Method. Butterworths, London.
23. Lopes, H.F., West, M., 2004. Bayesian model assessment in factor analysis. Statistica Sinica 14, 41-67. (Pubitemid 38422407)
24. Rencher, A.C., 2002. Methods of Multivariate Analysis. Wiley, New York.
25. Schwarz, G., 1978. Estimating the dimension of a model. Annals of Statistics 6, 461-464.
26. Shao, J., 1997. An asymptotic theory for linear model selection. Statistica Sinica 7, 221-264. (Pubitemid 127473663)
27. Thomson, G.H., 1934. The meaning of i in the estimate of g. British Journal of Psychology 25, 92-99.
28. Tu, I.-P., Chen, H., Chen, X., 2009. An eigenvector variability plot. Statistica Sinica 19, 1741-1754.
29. Ye, J., 1998. On measuring and correcting the effects of data mining and model selection. Journal of the American Statistical Association 93, 120-131.